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Identifying primary public transit corridors using multi-source big transit data

Authors:	Tong Zhang Yicong Li Hui Yang Chenrong Cui Jing Li Qinghua Qiao

Institution:	1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Chinazhangt@whu.edu.cnhttps://orcid.org/0000-0002-0683-4669;3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;4. Department of Geography and the Environment, University of Denver, Denver, CO, USAhttps://orcid.org/0000-0002-9544-8240;5. Chinese Academy of Surveying &6. Mapping, Beijing, China

Abstract:	ABSTRACT Effective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management.

Keywords:	Transit corridors smart card data trajectory data clustering trip

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